code: def _x_and_filters(
      draw, dim: int = 2, transpose: bool = False, max_dilation=3
  ):
      if not isinstance(dim, int):
          dim = draw(dim)
      strides = draw(
          st.one_of(
              st.lists(
                  st.integers(min_value=1, max_value=3),
                  min_size=dim,
                  max_size=dim,
              ),
              st.integers(min_value=1, max_value=3),
          )
      )
      if not transpose:
          padding = draw(
              st.one_of(
                  (
                      st.sampled_from(["same", "valid"])
                      if strides == 1
                      else st.just("valid")
                  ),
                  st.integers(min_value=1, max_value=3),
                  st.lists(
                      st.integers(min_value=1, max_value=2),
                      min_size=dim,
                      max_size=dim,
                  ),
              )
          )
      else:
          padding = draw(
              st.one_of(
                  st.integers(min_value=1, max_value=3),
                  st.lists(
                      st.integers(min_value=1, max_value=2),
                      min_size=dim,
                      max_size=dim,
                  ),
              )
          )
      batch_size = draw(st.integers(1, 5))
      filter_dim = draw(
          helpers.get_shape(
              min_num_dims=dim,
              max_num_dims=dim,
              min_dim_size=1,
              max_dim_size=5,
          )
      )
      dtype = draw(helpers.get_dtypes("float", full=False))
      input_channels = draw(st.integers(1, 3))
      output_channels = draw(st.integers(1, 3))
      group_list = [i for i in range(1, 3)]
      if not transpose:
          group_list = list(
              filter(lambda x: (input_channels % x == 0), group_list)
          )
      else:
          group_list = list(
              filter(
                  lambda x: (output_channels % x**2 == 0), group_list
              )
          )
      fc = draw(st.sampled_from(group_list))
      dilations = draw(
          st.one_of(
              st.lists(
                  st.integers(min_value=1, max_value=max_dilation),
                  min_size=dim,
                  max_size=dim,
              ),
              st.integers(min_value=1, max_value=max_dilation),
          )
      )
      fdilations = (
          [dilations] * dim if isinstance(dilations, int) else dilations
      )
      if transpose:
          x_dim = draw(
              helpers.get_shape(
                  min_num_dims=dim,
                  max_num_dims=dim,
                  min_dim_size=2,
                  max_dim_size=5,
              )
          )
      else:
          x_dim = []
          for i in range(dim):
              min_x = filter_dim[i] + (filter_dim[i] - 1) * (
                  fdilations[i] - 1
              )
              x_dim.append(draw(st.integers(min_x, 15)))
          x_dim = tuple(x_dim)
      if not transpose:
          output_channels = output_channels * fc
          filter_shape = (
              output_channels,
              input_channels // fc,
          ) + filter_dim
      else:
          input_channels = input_channels * fc
          filter_shape = (
              input_channels,
              output_channels // fc,
          ) + filter_dim
      x_shape = (batch_size, input_channels) + x_dim
      vals = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=x_shape,
              min_value=0.0,
              max_value=1.0,
          )
      )
      filters = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=filter_shape,
              min_value=0.0,
              max_value=1.0,
          )
      )
      bias = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=(output_channels,),
              min_value=0.0,
              max_value=1.0,
          )
      )
      if transpose:
          fstrides = (
              [strides] * dim if isinstance(strides, int) else strides
          )
          output_padding = draw(
              st.lists(
                  st.integers(min_value=1, max_value=2),
                  min_size=dim,
                  max_size=dim,
              )
          )
          padding = (
              [padding] * dim if isinstance(padding, int) else padding
          )
          for i in range(len(output_padding)):
              m = min(fstrides[i], fdilations[i])
              output_padding[i] = min(output_padding[i], m - 1)
          if draw(st.booleans()):
              output_padding = min(output_padding)
          assume(
              all(
                  s > 0
                  for s in _output_shape(
                      dim,
                      fdilations,
                      fstrides,
                      padding,
                      output_padding,
                      x_dim,
                      filter_dim,
                  )
              )
          )
          return (
              dtype,
              vals,
              filters,
              bias,
              dilations,
              strides,
              padding,
              output_padding,
              fc,
          )
      else:
          return (
              dtype,
              vals,
              filters,
              bias,
              dilations,
              strides,
              padding,
              fc,
          )
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_convolution_functions.py
imports: import math
  from hypothesis import strategies, assume
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_nn.test_layers import _assume_tf_dilation_gt_1
label_desc: Use image node_id 5 for calling a global function with example usage: _x_and_filters(draw, dim, transpose, max_dilation) and returns: dtype,  vals,  filters,  bias,  dilations,  strides,  padding,  output_padding,  fc, dtype,  vals,  filters,  bias,  dilations,  strides,  padding,  fc